Automated license plate recognition (hereinafter, “ALPR”) generally refers to an automated process for applying optical character recognition (hereinafter, “OCR”) techniques to images captured by traffic cameras to recognize vehicle license plate information.
ALPR technology is useful for law enforcement and other purposes, allowing for mass surveillance of vehicle traffic for a variety purposes at very low personnel costs. ALPR technology can be utilized concurrently with a variety of law enforcement procedures, such as techniques for determining vehicle speed, monitoring traffic signals, electronic toll collection, and individual vehicle surveillance.
ALPR methods can involve three steps. The first step can be determining the location of the license plate in the image (hereinafter, “plate localization”). The second step can be separating the individual characters on the license plate from the remainder of the image (hereinafter, “character segmentation”). The third step can be applying OCR techniques to the segmented characters.
Various image processing methods can be utilized as part of the ALPR process, including, for example, binarization. Generally, binarization is a process by which a color or gray-scale image can be analyzed, and a binary value can be assigned to each pixel of the image based on a set of parameters and the original color of the pixel. Such binary values can be visually depicted as black or white to create a monochromatic image. Therefore, a given pixel color can be assigned a “white” value or a “black” value during binarization.
Binarization of an image facilitates many processes that can be performed on the image. For example, a computing device can analyze binarization data and recognize clusters of adjacent pixels with the same binary value and match the clusters with known patterns of characters or objects.
However, the captured images of vehicle license plates are not always optimal for character recognition. For example, objects such as trailer hitches, rust, dirt, stickers, or/and license plate frames can occlude license plate characters from a camera's perspective. Additional factors, such as shadow and state license plate logos, can further slow or prevent the character recognition by hindering various ALPR sub-processes, such as binarization.
Such factors can be alleviated if an optimum set of parameters are utilized with the various ALPR sub-processes. For example, utilizing an optimum threshold value during the binarization process can resolve irregularities caused by factors such as shadowing and non-character objects. However, determining the optimum parameters is complicated by the fact that the optimum parameters can vary based on numerous factors, including time of day, license plate design, occlusion factors, position of the camera, and quality of the image.
Accordingly, APLR technology may be improved by techniques for dynamically determining optimum parameters for APLR sub-processes, such as binarization.